U.S. patent application number 17/304359 was filed with the patent office on 2021-12-23 for magnetic resonance system, image display method therefor, and computer-readable storage medium.
The applicant listed for this patent is GE Precision Healthcare LLC. Invention is credited to Nan CAO, Yongchuan LAI, Jianyong YIN.
Application Number | 20210393216 17/304359 |
Document ID | / |
Family ID | 1000005693285 |
Filed Date | 2021-12-23 |
United States Patent
Application |
20210393216 |
Kind Code |
A1 |
CAO; Nan ; et al. |
December 23, 2021 |
MAGNETIC RESONANCE SYSTEM, IMAGE DISPLAY METHOD THEREFOR, AND
COMPUTER-READABLE STORAGE MEDIUM
Abstract
Embodiments of the present invention provide a magnetic
resonance system, an image display method thereof, and a
computer-readable storage medium. The method includes: acquiring
sequential images to be displayed, the sequential images comprising
a plurality of images; determining an identical window width for
the plurality of images; and displaying the plurality of images of
the sequential images based on the window width.
Inventors: |
CAO; Nan; (Beijing, CN)
; LAI; Yongchuan; (Beijing, CN) ; YIN;
Jianyong; (Beijing, CN) |
|
Applicant: |
Name |
City |
State |
Country |
Type |
GE Precision Healthcare LLC |
Wauwatosa |
WI |
US |
|
|
Family ID: |
1000005693285 |
Appl. No.: |
17/304359 |
Filed: |
June 18, 2021 |
Current U.S.
Class: |
1/1 |
Current CPC
Class: |
A61B 5/7264 20130101;
A61B 5/055 20130101; G01R 33/5602 20130101; A61B 5/742
20130101 |
International
Class: |
A61B 5/00 20060101
A61B005/00; A61B 5/055 20060101 A61B005/055; G01R 33/56 20060101
G01R033/56 |
Foreign Application Data
Date |
Code |
Application Number |
Jun 23, 2020 |
CN |
202010582255.9 |
Claims
1. An image display method for a magnetic resonance system, the
method comprising: acquiring sequential images to be displayed, the
sequential images comprising a plurality of images; determining an
identical window width for the plurality of images; and displaying
the plurality of images of the sequential images based on the
window width.
2. The method according to claim 1, wherein the determining an
identical window width for the plurality of images comprises:
acquiring a sorting result of pixel values of the plurality of
images; and determining the window width based on a pixel value at
a preset ordinal position in the sorting result or a plurality of
pixel values within a preset ordinal position range.
3. The method according to claim 2, wherein the determining an
identical window width for the plurality of images comprises:
determining the window width based on a pixel value at an
intermediate ordinal position in the sorting result or a plurality
of pixel values within an intermediate ordinal position range
containing the intermediate ordinal position.
4. The method according to claim 1, wherein the determining the
window width based on the plurality of pixel values within the
intermediate ordinal position range comprises: determining an
average of the plurality of pixel values as the window width.
5. The method according to claim 1, further comprising: determining
an adjustment factor, and adjusting the window width based on the
adjustment factor, wherein the displaying the plurality of images
of the sequential images based on the identical window width
comprises: displaying the plurality of images based on the adjusted
window width.
6. The method according to claim 5, wherein the determining an
adjustment factor comprises: determining the adjustment factor
based on one or more pieces of imaging information corresponding to
the sequential images.
7. The method according to claim 6, wherein the one or more pieces
of imaging information comprise one or more of an imaging site, a
scan sequence, a scan plane, an echo time, an inversion time, and a
repetition time configured by the magnetic resonance system when
generating the sequential images.
8. The method according to claim 7, wherein the determining an
adjustment factor comprises: inputting the one or more pieces of
imaging information into a predetermined deep learning network, and
outputting the adjustment factor through the deep learning
network.
9. The method according to claim 5, wherein the step of determining
the adjustment factor comprises: determining user information for
displaying the sequential images; and determining a corresponding
adjustment factor based on the determined user information.
10. The method according to claim 5, wherein the step of
determining the adjustment factor comprises: determining user
information for displaying the sequential images; and inputting the
determined user information into a predetermined second deep
learning network, and outputting the adjustment factor through the
second deep learning network.
11. A magnetic resonance system, comprising: a scanner configured
to generate sequential images by performing magnetic resonance
scanning on an imaging site, the sequential images comprising a
plurality of images; a processor configured to acquire the
sequential images and determine an identical window width for the
plurality of images; and a display unit displaying the plurality of
images of the sequential images based on the identical window
width.
12. A computer-readable storage medium for storing
computer-readable instructions, wherein the computer-readable
instructions are configured to perform the image display method
according to claim 1.
Description
CROSS-REFERENCE TO RELATED APPLICATIONS
[0001] This application claims priority pursuant to 35 U.S.C.
119(a) to China Patent Application No. 202010582255.9 filed on Jun.
23, 2020, the disclosure of which is herein incorporated by
reference in its entirety.
TECHNICAL FIELD
[0002] Embodiments disclosed in the present invention relate to
medical imaging technologies, and more particularly relate to a
magnetic resonance system, an image display method therefor, and a
computer-readable storage medium.
BACKGROUND
[0003] In the prior art, the magnetic resonance imaging (MRI)
technology can be used to perform imaging on human tissues to
obtain a plurality of cross-sectional images, i.e., an image
sequence, of a region of interest. At a certain stage of magnetic
resonance examination, the image sequence needs to be displayed on
the human-computer interaction interface of a magnetic resonance
system in a certain arrangement manner, so as to facilitate reading
and viewing operations by physicians. In practical applications,
when reading sequential images, physicians often need to draw on
experience to manually adjust display parameters, for example, a
window width, of one or more images in the image sequence because
of great brightness differences between various images in the image
sequence or of an unsatisfactory display effect for clinical
diagnosis. This manner affects the efficiency of magnetic resonance
imaging-based diagnosis.
SUMMARY
[0004] An embodiment of the present invention provides an image
display method for a magnetic resonance system, the method
comprising:
[0005] acquiring sequential images to be displayed, the sequential
images comprising a plurality of images;
[0006] determining an identical window width for the plurality of
images; and
[0007] displaying the plurality of images of the sequential images
based on the window width.
[0008] In one embodiment, the determining an identical window width
for the plurality of images further comprises:
[0009] acquiring a sorting result of pixel values of the plurality
of images; and
[0010] determining the window width based on a pixel value at a
preset ordinal position in the sorting result or a plurality of
pixel values within a preset ordinal position range.
[0011] In one embodiment, the window width is determined based on a
pixel value at an intermediate ordinal position in the sorting
result or a plurality of pixel values within an intermediate
ordinal position range containing the intermediate ordinal
position.
[0012] In one embodiment, the determining the window width based on
the plurality of pixel values within the intermediate ordinal
position range comprises: determining an average of the plurality
of pixel values as the window width.
[0013] The method may further comprise: determining an adjustment
factor, and adjusting the window width based on the adjustment
factor. In one embodiment, the plurality of images are displayed
based on the adjusted window width.
[0014] In one embodiment, the adjustment factor is determined based
on one or more pieces of imaging information corresponding to the
sequential images.
[0015] The one or more pieces of imaging information comprise one
or more of an imaging site, a scan sequence, a scan plane, an echo
time, an inversion time, and a repetition time configured by the
magnetic resonance system when generating the sequential
images.
[0016] In one embodiment, the one or more pieces of imaging
information are input into a predetermined deep learning network,
and the adjustment factor is output through the deep learning
network.
[0017] In one embodiment, the step of determining the adjustment
factor comprises:
[0018] determining user information for displaying the sequential
images; and determining a corresponding adjustment factor based on
the determined user information.
[0019] In one embodiment, the determined user information is input
into a predetermined second deep learning network, and the
adjustment factor is output through the second deep learning
network.
[0020] Another embodiment of the present invention further provides
a magnetic resonance system, comprising:
[0021] a scanner configured to generate sequential images by
performing magnetic resonance scanning on an imaging site, the
sequential images comprising a plurality of images;
[0022] a processor configured to acquire the sequential images and
determine an identical window width for the plurality of images;
and a display unit displaying the plurality of images of the
sequential images based on the identical window width.
[0023] Another embodiment of the present invention further provides
a computer-readable storage medium for storing computer-readable
instructions, wherein the computer-readable instructions are
configured to perform the image display method according to any one
of the embodiments described above.
[0024] It should be understood that the brief description above is
provided to introduce, in a simplified form, some concepts that
will be further described in the Detailed Description. The brief
description above is not meant to identify key or essential
features of the claimed subject matter. The protection scope is
defined uniquely by the claims that follow the detailed
description. Furthermore, the claimed subject matter is not limited
to implementations that solve any disadvantages noted above or in
any section of the present disclosure.
BRIEF DESCRIPTION OF THE DRAWINGS
[0025] The present invention will be better understood by reading
the following description of non-limiting embodiments with
reference to the accompanying drawings, where
[0026] FIG. 1 shows a schematic structural diagram of a magnetic
resonance system;
[0027] FIG. 2 shows a flowchart of an image display method
according to an embodiment of the present invention;
[0028] FIG. 3 shows a flowchart of an image display method
according to another embodiment of the present invention;
[0029] FIG. 4 shows a flowchart of an image display method
according to another embodiment of the present invention;
[0030] FIG. 5 shows a flowchart of an image display method
according to another embodiment of the present invention;
[0031] FIG. 6 shows a flowchart of an image display method
according to another embodiment of the present invention;
[0032] FIG. 7 shows a flowchart of an image display method
according to another embodiment of the present invention; and
[0033] FIG. 8 shows a flowchart of an image display method
according to another embodiment of the present invention.
DETAILED DESCRIPTION
[0034] FIG. 1 shows a schematic structural diagram of a magnetic
resonance system, and the magnetic resonance system 100 comprises a
scanner 110. The scanner 110 is configured to perform magnetic
resonance scanning on an object (for example, a human body) 16 to
generate image data of a region of interest of the object 16, and
the region of interest may be a predetermined imaging site or
tissue to be imaged. The image data may be sequential images having
a plurality of images. In one embodiment, the plurality of images
may be two-dimensional images corresponding to a plurality of
cross-section (or tomographic) positions of the region of
interest.
[0035] The magnetic resonance system may include a controller 120,
which is coupled to the scanner 110 to control the scanner 110 to
perform the aforementioned magnetic resonance scanning process.
Specifically, the controller 120 may send a sequence control signal
to relevant components (such as a radio-frequency generator and a
gradient coil driver that will be described below) of the scanner
110 through a sequence generator (not shown), so that the scanner
110 performs the preset scan sequence.
[0036] Those skilled in the art could understand that the "scan
sequence" refers to a combination of pulses having specific
amplitudes, widths, directions, and timings that are applied while
performing a magnetic resonance imaging scan. The pulses may
typically include, for example, a radio-frequency pulse and a
gradient pulse. The radio-frequency pulse may include, for example,
a radio-frequency transmit pulse for exciting protons in the human
body to resonate, and the gradient pulse may include, for example,
a slice selection gradient pulse, a phase encoding gradient pulse,
and a frequency encoding gradient pulse. Typically, a plurality of
scanning sequences may be pre-configured in the magnetic resonance
system, so that a sequence adapted for clinical testing
requirements is selectable. The clinical testing requirements may
include, for example, an imaging site, an imaging function,
etc.
[0037] In practice, it may be required to select different scan
sequence types depending on different clinical applications, for
example, an echo planar imaging (EPI) sequence, a gradient echo
(GRE) sequence, a spin echo (SE) sequence, a fast spin echo (FSE)
sequence, a diffusion-weighted imaging (DWI) sequence, an inversion
recovery (IR) sequence, and the like; and in different clinical
applications, each scan sequence may have different scan sequence
parameters, for example, a T1-weighted value, a T2-weighted value,
an echo time, a repetition time, an inversion recovery time,
etc.
[0038] In an example, the scanner 110 may include a main magnet
assembly 111, a table 112, a radio-frequency generator 113, a
radio-frequency transmitting coil 114, a gradient coil driver 115,
a gradient coil assembly 116, and a data acquisition unit 117.
[0039] The main magnet assembly 111 usually includes an annular
superconducting magnet defined in a housing. The annular
superconducting magnet is mounted in an annular vacuum container.
The annular superconducting magnet and the housing thereof define a
cylindrical space, for example, a scanning chamber 118 shown in
FIG. 1, surrounding the object 16. The main magnet assembly 111
generates a constant magnetic field, i.e., a B0 field, in a Z
direction of the scanning chamber 118. Typically, a uniform portion
of the B0 field is formed in a central region of the main
magnet.
[0040] The table 112 is configured to carry the object 16, and
travel in the Z direction to enter or exit the scanning chamber 118
in response to the control of the controller 120. For example, in
an embodiment, an imaging volume of the object 16 may be positioned
at a central region of the scanning chamber with uniform magnetic
field strength so as to facilitate scanning imaging of the imaging
volume of the object 16.
[0041] The magnetic resonance system transmits a static magnetic
pulse signal to the object 16 located in the scanning chamber by
using the formed B0 field, so that protons in a resonance volume
within the body of the object 16 precess in an ordered manner to
generate a longitudinal magnetization vector.
[0042] The radio-frequency generator 113 is configured to generate
a radio-frequency pulse, for example, a radio-frequency excitation
pulse, in response to a control signal of the controller 120. The
radio-frequency excitation pulse is amplified (for example, by a
radio-frequency power amplifier (not shown)) and then applied to
the radio-frequency transmitting coil 114, so that the
radio-frequency transmitting coil 114 emits to the object 16 a
radio-frequency field B1 orthogonal to the B0 field to excite
nuclei in the aforementioned resonant volumes, and generate a
transverse magnetization vector.
[0043] The radio-frequency transmitting coil 114 may include, for
example, a body coil disposed along an inner circumference of the
main magnet, or a head coil dedicated to head imaging. The body
coil may be connected to a transmitting/receiving (T/R) switch (not
shown). The transmitting/receiving switch is controlled so that the
body coil can be switched between a transmitting mode and a
receiving mode. In the receiving mode, the body coil may be
configured to receive a magnetic resonance signal from the object
16.
[0044] After the end of the radio-frequency excitation pulse, a
free induction decay signal, namely, a magnetic resonance signal
that can be acquired, is generated in the process that the
transverse magnetization vector of the object 16 is gradually
restored to zero.
[0045] The gradient coil driver 115 is configured to provide a
suitable current/power to the gradient coil assembly 116 in
response to a gradient pulse control signal or a shimming control
signal sent by the controller 120.
[0046] The gradient coil assembly 116, on one hand, forms a varying
magnetic field in an imaging space so as to provide
three-dimensional position information to the magnetic resonance
signal, and on the other hand, generates a compensating magnetic
field of the B0 field to shim the B0 field.
[0047] The gradient coil assembly 116 may include three gradient
coils. The three gradient coils are respectively configured to
generate magnetic field gradients inclined to three spatial axes
(for example, X-axis, Y-axis, and Z-axis) perpendicular to each
other. More specifically, the gradient coil assembly 116 applies a
magnetic field gradient in a slice selection direction (Z
direction) so as to select a layer in the imaging volume. Those
skilled in the art understand that the layer is any one of a
plurality of two-dimensional slices distributed in the Z direction
in the three-dimensional imaging volume. When the imaging is
scanned, the radio-frequency transmitting coil 114 transmits a
radio-frequency excitation pulse to the layer of the imaging volume
and excites the layer. The gradient coil assembly 116 applies a
magnetic field gradient in a phase encoding direction (Y direction)
so as to perform phase encoding on a magnetic resonance signal of
the excited layer. The gradient coil assembly 116 applies a
gradient field in a frequency encoding direction of the object 16
so as to perform frequency encoding on the magnetic resonance
signal of the excited layer.
[0048] The data acquisition unit 117 is configured to acquire the
magnetic resonance signal (for example, received by the body coil
or a surface coil) in response to a data acquisition control signal
of the controller 120. In an embodiment, the data acquisition unit
117 may include, for example, a radio-frequency preamplifier, a
phase detector, and an analog/digital converter, where the
radio-frequency preamplifier is configured to amplify the magnetic
resonance signal, the phase detector is configured to perform phase
detection on the amplified magnetic resonance signal, and the
analog/digital converter is configured to convert the
phase-detected magnetic resonance signal from an analog signal to a
digital signal.
[0049] The magnetic resonance system 100 includes an image
reconstruction unit 130, which may reconstruct, based on the
aforementioned digitized magnetic resonance signal, a series of
two-dimensional cross-sectional images of an imaging volume of the
object 16, i.e., the image sequence. Specifically, the
reconstruction unit may perform the image reconstruction described
above based on communication with the controller 120.
[0050] The magnetic resonance system 100 includes a processing unit
140, which may perform any required image processing on any image
in the image sequence, for example, image correction, image display
parameter determination, etc. The image processing described above
may be an improvement or adaptive adjustment made to an image in
terms of any one of contrast, uniformity, sharpness, brightness,
etc. Specifically, the processing unit 140 may perform the image
processing described above based on communication with the
controller 120.
[0051] In one embodiment, the controller 120, the image
reconstruction unit 130, and the processing unit 140 may separately
or collectively include a computer and a storage medium, and the
storage medium records a predetermined control program or data
processing program to be executed by the computer. For example, the
storage medium may store a program configured to implement imaging
scanning, image reconstruction, image processing, etc. For example,
the storage medium may store a program configured to implement the
image display method of the embodiments of the present invention.
The storage medium may include, for example, a ROM, a floppy disk,
a hard disk, an optical disk, a magneto-optical disk, a CD-ROM, or
a non-volatile memory card.
[0052] The magnetic resonance system 100 may include a display unit
150, which may be configured to display an operation interface and
various data or images generated during a data processing process.
The display unit 150 may display, in response to a display control
signal of the controller 120 (the display control signal may be
generated in response to a request operation of a physician for
reading images), the sequential images through the display unit 150
in a certain arrangement manner. For example, the images may be
arranged according to the order of cross-section positions. In
addition, the display unit 150 may also communicate with the
processing unit 140 (for example, via the controller 120) to
display the sequential images in accordance with display parameters
determined by the processing unit 140.
[0053] When determining the display parameters of the sequential
images, the processing unit 140 may specifically consider
parameters related to contrast, sharpness, brightness, uniformity,
etc., for example, a window width and a window level. Those skilled
in the art would understand that the window width corresponds to an
image pixel range, and the window level corresponds to the middle
value in the pixel range. It is typically desirable to set the
window level at a tissue most capable of reflecting a lesion (for
example, a soft tissue, a bone, blood, etc.), and a window level
closer to pixel values of the tissue yields better uniformity of
the image. With that window level being set as the center, a
smaller window width yields a higher contrast ratio.
[0054] The magnetic resonance system 100 includes a console 160,
which may include a user input device, such as a keyboard, a mouse,
etc. The controller 120 may communicate with the scanner 110, the
image reconstruction unit, the processing unit 140, the display
unit 150, etc, in response to a control command generated by a user
based on operating the console 160 or an operation dashboard/button
and the like disposed on a housing of a main magnet.
[0055] Typically, respective window widths and window levels are
configured for all of the images, so that each image in the
sequential images presented to the user is expected to have ideal
image quality. For example, a corresponding window width is
configured based on a maximum pixel value and a minimum pixel value
of each image, and a window level is configured based on the window
width. For example, the window level is a median value of the
window width. However, in practice, a small amount of metal,
vascular tissues, etc., in the human body might cause a
high-brightness signal in a portion of an image, causing the
maximum pixel value in the image to be excessively high, and
leading to an excessively large window width. Accordingly, the
obtained window level deviates significantly from pixel values of a
tissue to be viewed, so that a portion of the images in the image
sequence are too dark, and overall, large brightness differences
within the image sequence are yielded, resulting in reading
difficulties for physicians.
[0056] FIG. 2 shows a flowchart of an image display method
according to an embodiment of the present invention. As shown in
FIG. 2, the method includes steps S21, S23, and S25.
[0057] In step S21, sequential images to be displayed are acquired,
where the sequential images include a plurality of images. For
example, upon receiving a reading request of a user, the controller
120 may notify the processing unit 140 to retrieve a plurality of
cross-sectional images generated from a magnetic resonance scan
performed by the scanner 110 on a region of interest of the object
16, or to retrieve the plurality of cross-sectional images that
have undergone image processing.
[0058] In step S25, an identical window width is determined for the
plurality of images, and the determined window width is sent to the
display unit 160 directly or through the controller 120.
[0059] In step S29, the plurality of images of the sequential
images are displayed based on the identical window width.
Specifically, when displaying the plurality of images, the display
unit 160 may display pixel values outside of the pixel range
defined by the window width as a background color, for example, the
pixel values being "0".
[0060] Setting an identical window width for a plurality of images
in an image sequence prevents problems such as reading difficulties
caused by excessive brightness differences between images under
display, the need to perform corresponding manual adjustment on
different images, etc. Setting a window level based on the
identical window width also prevents the problem of poor uniformity
between images.
[0061] FIG. 3 shows a flowchart of an image display method
according to another embodiment of the present invention. As shown
in FIG. 3, the method includes steps S21, S33, S35, and S29.
[0062] In step S33, a sorting result of the pixel values of the
plurality of images is acquired. For example, in the case that all
of the pixel values of the plurality of images are counted to
indicate that a total of N pixel values are included, then the N
pixel values are sorted in a descending order or an ascending order
(1, 2, 3 . . . m-2, m-1, m, m+1, m+2 . . . N), where m is a natural
number between 1 and N.
[0063] In step S35, the window width is determined based on a pixel
value at a preset ordinal position in the sorting result (for
example, the m-th pixel value) or a plurality of pixel values
within a preset ordinal position range (for example, from the
(m-2)-th pixel value to the (m+2)-th pixel value, or the (q+)-th
pixel value). Preferably, the preset ordinal position may be an
intermediate ordinal position. For example, a window width may be
determined based on a pixel value arranged at the intermediate
ordinal position, or the window width may be determined based on a
plurality of pixel values arranged within an intermediate range
(i.e., an intermediate ordinal position range containing the
intermediate ordinal position).
[0064] In a specific embodiment, the preset signal or the pixel
value at the intermediate ordinal position may be directly set as
the window width, or an average of the plurality of pixel values in
a preset range or the intermediate ordinal position range may be
set as the window width.
[0065] In this embodiment, a uniform window width may be configured
for the plurality of images of the image sequence without complex
computation. Moreover, since the selection is performed in
accordance with the ordinal position of the pixel value(s) instead
of merely relying on the maximum pixel value and the minimum pixel
value, the problem of excessive image brightness or darkness caused
by an excessively large or small maximum pixel value is
prevented.
[0066] FIG. 4 shows a flowchart of an image display method
according to another embodiment of the present invention. The image
display method of this embodiment includes step S21, step S43, step
S45, and step S47. Step S43 may be similar to step S25, for
example, determining an identical window width for the plurality of
images. More specifically, step S43 may also include steps S33 and
S35 so as to determine the identical window width for the plurality
of images.
[0067] In step S45, an adjustment factor is determined based on one
or more pieces of imaging information corresponding to the
sequential images, and the window width is adjusted based on the
adjustment factor. The imaging information may include, for
example, one or more of an imaging site, a scan sequence, a scan
plane, an echo time, an inversion recovery time, and a repetition
time configured by the magnetic resonance system when generating
the sequential images.
[0068] Those skilled in the art would understand that: the imaging
site may include a body part of a human body, for example, a head,
an abdomen, a chest, a heart, and the like; the scanning plane is a
scanning layer and corresponds to a two-dimensional cross-sectional
image in an imaging sequence, and each scanning layer/plane has a
specific cross-section position; the echo time refers to a time
range from a radio-frequency excitation pulse to the center of an
echo signal in the scanning sequence; the inversion time is a time
range between the center of a 180.degree. inversion pulse and the
center of a 90.degree. excitation pulse; and the repetition time is
a time range between the centers of two adjacent excitation
pulses.
[0069] The adjustment factor may be determined based on weights of
one or more pieces of the imaging information described above. The
adjustment described above may specifically include multiplying the
adjustment factor with the window width. In a specific example, the
adjustment factor may be less than or greater than 1.
[0070] In step S47, the plurality of images of the sequential
images are displayed based on the determined window width, and
specifically, the plurality of images are displayed based on the
adjusted window width.
[0071] In this way, a suitable display manner may be selected for
different imaging information to better meet clinical diagnostic
requirements. For example, the adjustment factor may be different
due to different imaging sites, since a higher image brightness may
be required for reading images of one imaging site to make a lesion
more readily observable, while a lower image brightness is required
for reading images of another imaging site to make a lesion more
readily observable. As another example, for the same imaging site,
the brightness requirement for reading may also vary if a different
scanning sequence or different scanning parameter is adopted, and a
corresponding weight is used to determine a suitable window width
adjustment factor to meet a corresponding requirement.
[0072] FIG. 5 shows a flowchart of an image display method
according to another embodiment of the present invention.As shown
in FIG. 5, the image display method of this embodiment includes
step S21, step S43, step S55, and step S47. In step S55, one or
more pieces of imaging information are input into a predetermined
first deep learning network, an adjustment factor is output through
the first deep learning network, and the window width determined in
step S43 is adjusted based on the adjustment factor.
[0073] Data training may be performed by using the following
exemplary method to obtain the first deep learning network
described above.
[0074] In one step, ideal window width adjustment factors for a
plurality of image sequences are acquired. For example, based on
any of the above embodiments, initial window widths may be first
determined for the respective image sequences, then the initial
window widths may be adjusted manually, so that corresponding image
sequences have ideal display brightness, then a plurality of window
width adjustment factors corresponding to the plurality of image
sequences are calculated based on the adjusted window widths and
the initial window widths.
[0075] In another step, imaging information corresponding to the
plurality of image sequences is determined, where the imaging
information respectively corresponding to the respective image
sequences may be one single type of information or different
combinations of multiple types of information.
[0076] In another step, the plurality of window width adjustment
factors are used as an output dataset, the imaging information
corresponding to the plurality of image sequences is used as an
input dataset, and a suitable machine learning network is selected
to perform machine learning, so as to assign a network parameter
associated with the input dataset and the output dataset to the
machine learning network.
[0077] The trained first learning network enables, when displaying
an image sequence, automatic acquisition of a window width adapted
to imaging information thereof, so as to better meet the display
requirement for clinical diagnosis.
[0078] FIG. 6 shows a flowchart of an image display method of
another embodiment of the present invention. As shown in FIG. 6,
the image display method of this embodiment includes step S21, step
S43, step S65, step S66, and step S47. In step S65, user
information for displaying the sequential images is determined. In
step S66, an adjustment factor is determined based on the
determined user information, and the window width determined in
step S43 is adjusted based on the adjustment factor. For example, a
plurality of corresponding adjustment factors may be determined
respectively based on multiple pieces of user information. In this
way, when an image sequence is displayed, a window width adjustment
factor may be automatically configured based on a personal habit or
preference of a user to better meet a personalized display
requirement of the user.
[0079] In this embodiment, the adjustment factor corresponding to
user information may be stored in advance, and when a user performs
a reading operation, information of the user is identified, so that
the corresponding adjustment factor may be retrieved to adjust an
initial window width.
[0080] FIG. 7 shows a flowchart of an image display method of
another embodiment of the present invention. As shown in FIG. 7,
the image display method of this embodiment includes step S21, step
S43, step S65, step S76, and step S47. In step S76, the determined
user information is input into a predetermined second deep learning
network, an adjustment factor is output through the second deep
learning network, and the window width determined in step S43 is
adjusted based on the adjustment factor.
[0081] Data training may be performed by using the following
exemplary method to obtain the second deep learning network
described above.
[0082] In one step, a plurality of window width adjustment factors
for reading operations of a plurality of users may be acquired for
one or a plurality of image sequences. For example, respective
adjusted window widths during reading operations of the users and
initial window widths prior to the reading operations may be
acquired, and the window width adjustment factors corresponding to
the users are calculated based on the adjusted window widths and
the initial window widths.
[0083] In another step, the plurality of window width adjustment
factors are used as an output dataset, the user information of the
plurality of users is used as an input dataset, and a suitable
machine learning network is selected to perform machine learning,
so as to assign a network parameter associated with the input
dataset and the output dataset to the machine learning network.
[0084] The trained second learning network enables, when displaying
an image sequence, quick and accurate configuration of a window
width adjustment factor based on a personal habit or preference of
a user.
[0085] FIG. 8 shows a flowchart of an image display method of
another embodiment of the present invention. As shown in FIG. 8,
the image display method of this embodiment includes step S21, step
S43, step S85, and step S47. In step S85, an adjustment factor is
determined based on one or more pieces of imaging information
corresponding to the sequential images and current user
information, and the window width is adjusted based on the
adjustment factor. In this case, the determined adjustment factor
may at least include a component related to the imaging information
and a component related to the user information. For example, in
this step, mathematical computation may be further performed on the
adjustment factor determined based on the imaging information and
the adjustment factor determined based on the user information to
obtain an overall adjustment factor, and the window width may be
adjusted by using the overall adjustment factor. Alternatively, the
window width may be first adjusted based on either the imaging
information or the user information to obtain an intermediate value
of the window width, and then the intermediate value may be further
adjusted based on the other of these two to obtain an ultimate
window width.
[0086] Further, a third deep learning network may be utilized to
obtain the adjustment factor in step S85. For example, the current
user information and the imaging information are input into a
predetermined third deep learning network, an adjustment factor is
output by the third deep learning network, and the window width
determined in step S43 is adjusted based on the adjustment
factor.
[0087] Data training may be performed by using the following
exemplary method to obtain the third deep learning network
described above.
[0088] In one step, ideal window width adjustment factors for a
plurality of image sequences are acquired. For example, initial
window widths may be first determined for the respective image
sequences, then adjustment factors used by a plurality of users for
adjusting the initial window widths are acquired, so that each of
the image sequences has a plurality of ideal display brightness
values for the plurality of users, and then a plurality of window
width adjustment factors corresponding to the respective image
sequences are calculated based on adjusted window widths and the
initial window widths, wherein the plurality of window width
adjustment factors correspond to multiple pieces of user
information, respectively.
[0089] In another step, a plurality of window width adjustment
factors of a plurality of image sequences are used as an output
dataset, the imaging information and user information corresponding
to the plurality of image sequences are used as an input dataset,
and a suitable machine learning network is selected to perform
machine learning, so as to assign a network parameters associated
with the input dataset and the output dataset to the machine
learning network.
[0090] The trained third deep learning network enables, when
displaying an image sequence, acquisition of a window width
adjustment factor capable of matching the imaging information so as
to meet a display requirement of clinical diagnosis while also
meeting a personal user preference.
[0091] In an embodiment of the present invention, the window level
is determined as a median value of the window width.
[0092] As discussed herein, the deep learning technology (also
referred to as deep machine learning, hierarchical learning, deep
structured learning, or the like) employs an artificial neural
network for learning. The deep learning method is characterized by
using one or a plurality of network architectures to extract or
simulate data of interest. The deep learning method may be
implemented using one or a plurality of processing layers (for
example, an input layer, an output layer, a convolutional layer, a
normalization layer, or a sampling layer, where processing layers
of different numbers and functions may exist according to different
deep learning network models), where the configuration and number
of the layers allow a deep learning network to process complex
information extraction and modeling tasks. Specific parameters (or
referred to as "weight" or "bias") of the network are usually
estimated through a so-called learning process (or training
process). The learned or trained parameters usually result in (or
output) a network corresponding to layers of different levels, so
that extraction or simulation of different aspects of initial data
or the output of a previous layer usually may represent the
hierarchical structure or concatenation of layers. Thus, processing
may be performed layer by layer. That is, "simple" features may be
extracted from input data for an earlier or higher-level layer, and
then these simple features are combined into a layer exhibiting
features of higher complexity. In practice, each layer (or more
specifically, each "neuron" in each layer) may process input data
as output data for representation using one or a plurality of
linear and/or non-linear transformations (so-called activation
functions). The number of the plurality of "neurons" may be
constant among the plurality of layers or may vary from layer to
layer.
[0093] As discussed herein, a training data set having known input
values (for example, known imaging information of an image
sequence, user information, etc.,) and known or expected output
values (for example, known ideal window width adjustment factors)
may be employed as part of initial training of a deep learning
process that solves a specific problem. In this manner, a deep
learning algorithm may process a known data set or training data
set (in a supervised or guided manner or an unsupervised or
unguided manner), until a mathematical relationship between initial
data and an expected output is identified and/or a mathematical
relationship between the input and output of each layer is
identified and characterized. (Partial) input data is usually used,
and a network output is created for the input data in the learning
process. Afterwards, the created output is compared with the
expected (target) output of the data set, and then a generated
difference from the expected output is used to iteratively update
network parameters (weight and offset). One such update/learning
mechanism uses a stochastic gradient descent method to update a
network parameter. Apparently, those skilled in the art should
understand that other methods known in the art may also be
utilized. Similarly, a separate validation data set may be used,
where both an input and an expected target value are known, but
only an initial value is provided to a trained deep learning
algorithm, and then an output is compared with an output of the
deep learning algorithm to validate prior training and/or prevent
excessive training.
[0094] Based on the description above, an embodiment of the present
invention may further provide a computer-readable storage medium in
which computer-readable instructions are stored, and the
computer-readable instructions are configured to control a magnetic
resonance scanning system to perform the image display method
according to any one of the embodiments described above. The
computer-readable storage medium may be similar to the storage
medium in the controller 120 in the system shown in FIG. 1.
[0095] As used herein, an element or step described as singular and
preceded by the word "a" or "an" should be understood as not
excluding such element or step being plural, unless such exclusion
is explicitly stated. Furthermore, references to "one embodiment"
of the present invention are not intended to be interpreted as
excluding the existence of additional embodiments that also
incorporate the recited features. Moreover, unless explicitly
stated to the contrary, embodiments "comprising," "including," or
"having" an element or a plurality of elements having a specific
property may include additional elements that do not have such
property. The terms "including" and "in which" are used as the
plain-language equivalents of the respective terms "comprising" and
"wherein". Furthermore, in the appended claims, the terms "first",
"second," "third" and so on are used merely as labels, and are not
intended to impose numerical requirements or a specific positional
order on their objects.
[0096] This written description uses examples to disclose the
present invention, including the best mode, and also to enable
those of ordinary skill in the relevant art to implement the
present invention, including making and using any devices or
systems and performing any incorporated methods. The patentable
scope of the present invention is defined by the claims, and may
include other examples that occur to those skilled in the art. Such
other examples are intended to be within the scope of the claims if
they have structural elements that do not differ from the literal
language of the claims, or if they include equivalent structural
elements without substantial differences from the literal language
of the claims.
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